{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0065a29e-b528-40cb-9b14-5eeb94c98e84",
   "metadata": {},
   "source": [
    "# MLflow and neuralforecast\n",
    "> Log your neuralforecast experiments to MLflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4fb1958",
   "metadata": {},
   "source": [
    "## Installing dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "963311a3-2427-4694-981b-438fce1c2981",
   "metadata": {},
   "source": [
    "To install Neuralforecast refer to https://nixtlaverse.nixtla.io/neuralforecast/examples/installation.html.\n",
    "\n",
    "To install mlflow: `pip install mlflow`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1125a0bc",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "687f6677",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import mlflow\n",
    "import mlflow.data\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from mlflow.client import MlflowClient\n",
    "from mlflow.data.pandas_dataset import PandasDataset\n",
    "from utilsforecast.plotting import plot_series\n",
    "\n",
    "from neuralforecast.core import NeuralForecast\n",
    "from neuralforecast.models import NBEATSx\n",
    "from neuralforecast.utils import AirPassengersDF\n",
    "from neuralforecast.losses.pytorch import MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbafb920-4af8-49d0-abfa-2ff79f2cfd00",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['NIXTLA_ID_AS_COL'] = '1'\n",
    "logging.getLogger(\"mlflow\").setLevel(logging.ERROR)\n",
    "logging.getLogger(\"pytorch_lightning\").setLevel(logging.ERROR)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2570db3c-d8f7-4b07-bd43-d79d730b07cb",
   "metadata": {},
   "source": [
    "## Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d556dc4-8965-4dfd-809a-7b87d348d5ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-08-31</td>\n",
       "      <td>606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-09-30</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-10-31</td>\n",
       "      <td>461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-11-30</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-12-31</td>\n",
       "      <td>432.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     unique_id         ds      y\n",
       "139        1.0 1960-08-31  606.0\n",
       "140        1.0 1960-09-30  508.0\n",
       "141        1.0 1960-10-31  461.0\n",
       "142        1.0 1960-11-30  390.0\n",
       "143        1.0 1960-12-31  432.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data and declare panel dataset\n",
    "Y_df = AirPassengersDF\n",
    "Y_train_df = Y_df[Y_df.ds<='1959-12-31'] # 132 train\n",
    "Y_test_df = Y_df[Y_df.ds>'1959-12-31'] # 12 test\n",
    "Y_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "919711ee-6fe1-442b-a11f-6031cd4b4999",
   "metadata": {},
   "source": [
    "## MLflow UI\n",
    "Run the following command from the terminal to start the UI: `mlflow ui`. You can then go to the printed URL to visualize the experiments."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00768993-bcf7-43d5-9ac3-2f916e52acc6",
   "metadata": {},
   "source": [
    "## Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4467763",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 42\n"
     ]
    }
   ],
   "source": [
    "mlflow.pytorch.autolog(checkpoint=False)\n",
    "\n",
    "with mlflow.start_run() as run:\n",
    "    # Log the dataset to the MLflow Run. Specify the \"training\" context to indicate that the\n",
    "    # dataset is used for model training\n",
    "    dataset: PandasDataset = mlflow.data.from_pandas(Y_df, source=\"AirPassengersDF\")\n",
    "    mlflow.log_input(dataset, context=\"training\")\n",
    "\n",
    "    # Define and log parameters\n",
    "    horizon = len(Y_test_df)\n",
    "    model_params = dict(\n",
    "        input_size=1 * horizon,\n",
    "        h=horizon,\n",
    "        max_steps=300,  \n",
    "        loss=MAE(),\n",
    "        valid_loss=MAE(),  \n",
    "        activation='ReLU',\n",
    "        scaler_type='robust',\n",
    "        random_seed=42,\n",
    "        enable_progress_bar=False,\n",
    "    )\n",
    "    mlflow.log_params(model_params)\n",
    "\n",
    "    # Fit NBEATSx model\n",
    "    models = [NBEATSx(**model_params)]\n",
    "    nf = NeuralForecast(models=models, freq='M')           \n",
    "    train = nf.fit(df=Y_train_df, val_size=horizon)\n",
    "    \n",
    "    # Save conda environment used to run the model\n",
    "    mlflow.pytorch.get_default_conda_env()\n",
    "    \n",
    "    # Save pip requirements\n",
    "    mlflow.pytorch.get_default_pip_requirements()\n",
    "\n",
    "mlflow.pytorch.autolog(disable=True)\n",
    "\n",
    "# Save the neural forecast model\n",
    "nf.save(path='./checkpoints/test_run_1/',\n",
    "        model_index=None, \n",
    "        overwrite=True,\n",
    "        save_dataset=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f72c67d-8d93-4a93-aa15-632c1ca3ae00",
   "metadata": {},
   "source": [
    "## Forecasting the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62bec1b2-c07f-4ffd-8e2f-d2581ec3fcf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = nf.predict(futr_df=Y_test_df)\n",
    "plot_series(Y_train_df, Y_hat_df, palette='tab20b')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
